The large space of possible combinations of vehicles and resources makes tabular RL methods infeasible for this problem [1]}. This leads us to apply approximate solution methods by utilizing a DNN to represent the policy. DNN consists of a set of adjustable parameters \(\theta \) , i.e., \(\pi _\theta (a_t | s_t)\)  that maps a given state to action probabilities.
